This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
The 2-NN Rule for More Accurate NN Risk Estimation
January 1985 (vol. 7 no. 1)
pp. 107-112
Keinosuke Fukunaga, Department of Electrical Engineering, Purdue University, West Lafayette, IN 47907.
Thomas E. Flick, Department of Electrical Engineering, Purdue University, West Lafayette, IN 47907.
By proper design of a nearest-neighbor (NN) rule, it is possible to reduce effects of sample size in NN risk estimation. The 2-NN rule for the two-class problem eliminates the first-order effects of sample size. Since its asymptotic value is exactly half that of the 1-NN rule, it is possible to substitute the 2-NN rule for the 1-NN rule with a resultant increase in accuracy. For further stabilization of the risk estimate with respect to sample size, 2-NN polarization is suggested. Examples are included. The 2-NN approach is extended to M-class and 2k-NN.
Citation:
Keinosuke Fukunaga, Thomas E. Flick, "The 2-NN Rule for More Accurate NN Risk Estimation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 7, no. 1, pp. 107-112, Jan. 1985, doi:10.1109/TPAMI.1985.4767625
Usage of this product signifies your acceptance of the Terms of Use.